9 papers
Logarithmic derivatives of variational and singular stochastic partial differential equations
Ehsan Mirafzali, Frank Proske, Razvan Marinescu
For a stochastic partial differential equation posed on a Gelfand triple and satisfying the fully local monotone conditions of Röckner, Shang and Zhang, we compute the logarithmic…
Active Learning for Machine Learning Driven Molecular Dynamics
Kevin Bachelor, Sanya Murdeshwar, Daniel Sabo +1
Machine-learned coarse-grained (CG) potentials are fast, but degrade over time when simulations reach under-sampled bio-molecular conformations, and generating widespread all-atom…
Hessian Matching for Machine-Learned Coarse-Grained Molecular Dynamics
Sanya Murdeshwar, Sanjit Shashi, Kevin Bachelor +3
Coarse-grained (CG) molecular dynamics enables simulations of atomic systems such as biomolecules at timescales inaccessible to all-atom (AA) methods, but existing CG neural potent…
Score-Based Diffusion Models in Infinite Dimensions: A Malliavin Calculus Perspective
Ehsan Mirafzali, Frank Proske, Daniele Venturi +1
We study score-based diffusion modelling in infinite-dimensional separable Hilbert spaces through Malliavin calculus, extending the analysis of generative models beyond the finite-…
Spinverse: Differentiable Physics for Permeability-Aware Microstructure Reconstruction from Diffusion MRI
Prathamesh Pradeep Khole, Mario M. Brenes, Zahra Kais Petiwala +5
Diffusion MRI (dMRI) is sensitive to microstructural barriers, yet most existing methods either assume impermeable boundaries or estimate voxel-level parameters without recovering…
Holographic generative flows with AdS/CFT
Ehsan Mirafzali, Sanjit Shashi, Sanya Murdeshwar +3
We present a framework for generative machine learning that leverages the holographic principle of quantum gravity, or to be more precise its manifestation as the anti-de Sitter/co…